deep reinforcement learning tutorial

Smart Car self driving car + intensive learning reinforcement learning + neural network simulationHttps://github.com/MorvanZhou/my_research/tree/master/self_driving_research_DQNReinforcement learning for autonomous Driving obstacle avoidance using LIDARHttps://github.com/peteflorence/Machine-

1 Preface In the previous depth Enhancement Study Series, we have analyzed the DQN algorithm in detail, a value based algorithm, then today, we are working with you to analyze another algorithm in depth enhancement learning, that is, based on the policy gradient policy gradient algorithm. The actor-critic algorithm combined with the value based algorithm is the most effective depth-enhanced learning algorit

passage in paper:"We assume have access to a object detector that provides plausible object candidates."To be blunt is to give a target artificially. And then we'll train. (essentially nesting of two dqn)That's no point.This can be trained from the intuitive sense.But the meaning is relatively small.SummaryThis article is an exaggeration of the proposed level of DRL to solve the problem of sparse feedback, but in fact is not really a solution, the middle of the target is too artificial, not uni

1 Preface Deep reinforcement learning can be said to be the most advanced research direction in the field of depth learning, the goal of which is to make the robot have the ability of decision-making and motion control. The machine flexibility that human beings create is far lower than some low-level organisms, such a

corporal punishment, these algorithms are punished when they make the wrong predictions, and they get rewarded when they make the right predictions-that's the point of reinforcement. Combining deep learning with enhanced algorithms can defeat human champions in Weiqi and Atari games. Although this does not sound convincing enough, it is far superior to their pr

of epsilon items! If the epsilon value is too low, the data after the whitening will appear to be noisy; Conversely, if the epsilon value is too high, the albino data will be too blurry compared to the original data.Epsilon method of selection:A. Draw the eigenvalues of the data graphically; b. Select a characteristic value that is larger than most of the noise in the data to reflect the epsilon .2. How to adjust the epsilon specifically? I don't know, if I had a exercise, I'd be fine.2. When p

, but it does not matter, it is recommended to take a look at this big review every time, each time you will have a different harvest. If you find it hard to understand what others are writing, there are many videos on the web, such as Fudan UniversityProfessor Wulide's "Deep Learning course" Very easy to understand, watching his instructional video will have a better understanding of the many underlying pr

nature up. Anyway, I knew it was stunned, Ai people began to rave, all kinds of people, and then now this thing began to become hot, do not know will be like Google glasses. As for the development of DRL, let's look at how those individuals shout!Second,Scientific Review First to the Chinese, this analysis DRL more objective, the recommended index of 3 stars http://www.infoq.com/cn/articles/atari-reinforcement-

Why Study Reinforcement Learning Reinforcement Learning is one of the fields I ' m most excited about. Over the past few years amazing results like learning to play Atari Games from Raw Pixelsand Mastering the Game of Go have Gotten a lot of attention, but RL is also widely